Creating the Future of Brain-tech with Brain wave AI
Platform

Hideki Matsubara
CEO, PGV Inc.

1. Founding of PGVPGV is a startup enterprise from Osaka University,
established in September, 2016 to create a practicalimplementation of results from the research of Professor
T. Sekitani, of the Institute of Scientific and Industrial
Research (ISIR, or SANKEN) at Osaka University.
Professor Sekitani conducts research on flexible electronics
technologies, and he has attracted attention from aroundthe world in this field. He has achieved excellent research
results with technology using highly elastic electrodes toaccurately capture brain wave data, which is particularly
difficult to measure, even compared to other biological
data. PGV was established to implement a patch-type
electro-encephalograph (EEG) device based on theseresults. With this patch-type encephalograph device, PGV
aims to become the technology leader in the brain-techmarket.
2. Characteristics of brain wave data
With brain activity, tiny electrical currents flow inthe brain. Electroencephalograms (EEG) are a record ofelectrical activity produced by nerve cells in the brain ofa person or animal, taken using electrodes placed on thescalp or other locations. They have two characteristics thatdifferentiate them from other biological signals such aspulse, heart-rate or myoelectric (muscle) signals.
The first is that brain wave response is extremely fast.
During human activity, the brain must process and send
instructions to each part of the body before that activity.
For example, when one is nervous, the pulse and heart rateincrease, but the brain feels the nervousness first, and thensends instructions to the heart. Thus, its response is fasterthan pulse, heart rate or myoelectric signals.
The second characteristic of brain waves is that they
carry more information. Brain waves are signals thatchange with a modulation width of roughly 0 to 50 Hz.
They also change without regulation or pattern. For
example, when you are reading a book, watching a movie,or listening to music, the waveforms of brain waves aredifferent in each case. In contrast, pulse and heart rate
have a set pattern; a fixed waveform centered around 60 to
70 Hz, getting faster or slower according to level of stress(autonomic nerve action). Differences in the book, movie

and music experiences can be distinguished from brain
waves, but such differences cannot be classified easily frompulse or heart rate.
Brain waves have these characteristics, but they are
difficult to handle because they are extremely small signals
and have complex waveforms. Pulse and heart rate signal
strength is in the range from 1 to 10 mV, but brain wave
signals are only 1 to 50 μV, and unlike pulse and heartrate, brain waves also occur with an unlimited number ofwaveform patterns. They are also measured together withmyoelectric signals due to the movement of eyelids andeyeballs, and this adds to the complexity of the waveforms.
As a result waveforms can be different, even for the sameactivity or state, making analysis to select the common
components very difficult.
3. PGV Strengths
As can be seen from the above characteristics, usefuland effective utilization of brain waves requires 1) theability to accurately and easily measure brain waves, which
are very weak biological data, and 2) to overcome thedifficulty of analyzing these measurements.
For the first point, we have implemented a patch-type
EEG (Figure 1) product using flexible electrode sheet
material by combining Prof. Sekitani’s research results withexpertise from Nippon Mektron Ltd., a world leader inthe field of flexible printed circuits (and a member of theNOK group, which is a PGV shareholder). In the past,
brain waves were measured using large, medical EEGs,
but their range of use was limited because they required up
to 30 minutes to set up, they placed a heavy burden on thesubject, and they were very expensive systems. PGV’s patchtype encephalograph is extremely easy to use compared toconventional large EEGs, and is able to obtain brain wave
data without compromising accuracy. The electrode sheets
used to measure brain waves are a patented technologythat is an extremely thin 50 μm, has excellent elasticity,
clings tightly to the forehead, and fluctuates very little inresistance, so the subject’s minute brain wave signals can becaptured. The device also uses excellent noise processing
technology to achieve stable EEG readings.
For the second point, we have incorporated AI in
analysis of brain waves and are accumulating know-New Breeze Spring 2022

9■ Figure 1: PGV’s patch-type EEG

how in efficient analysis of brain wave data. Brain wave
f luctuations do not show patterns and appear to beirregular. As such, even when performing a frequency
analysis, there are many dimensions that are beyond
the scope that a person can understand. It is extremely
difficult to derive patterns linking brain wave changes
with changes in the body or mind. This has lead to use ofAI analysis. In AI analysis of brain waves, large amountsof brain wave data labeled with the person’s activity orstate are collected and they can be analyzed to makeassociations between the person’s activity or state of mind
and body and the brain waves. By developing many brain
wave AIs in this way, it may also become possible tounderstand various states of a person’s mind and body
objectively, by measuring their brain waves.
4. PGV’s business―Now and in the future
So far, most of PGV’s work has been in developing,
manufacturing and selling the patch-type EEG, andcontracting services related to brain wave analysis.
PVG’s patch-type EEG sensor is small and light10

New Breeze Spring 2022

weight (27 g), is hardly noticeable when worn and iscontrolled wirelessly, making it very easy to measure brain
waves. As of August, 2020, it has also received medical
device certification (Class II, 302AFBZX00079000) as
a telemetry EEG. It is helping to make clinical researchusing brain waves at universities and research facilities lessexpensive and more effective.
Our contract services in brain wave analysis involve
medical research and also a range of research involving thefive senses and other bodily states (fatigue, concentration,etc.). Our customers include university and other researchfacilities as well as enterprise R&D departments andnew-business development departments. We have been
performing frequency analysis of brain waves, but as
mentioned above, since FY2020, we have been using AI
based on the characteristics of brain waves.
To raise our competitiveness in the brain-tech market,
we are promoting a brain wave AI platform concept. The
platform is composed of three elements: (1) an EEG, (2)a brain wave AI development tool, and (3) brain wave AImodels (Figure 2).

■ Figure 2: Three elements of the brain wave AI platform

Regarding the first element, we have begun developinga next-generation EEG. We are pursuing a more compact
and user-friendly EEG while maintaining high accuracy,
to further advance the current EEG. In the future, weplan to develop an AI chip, and automate brain wave preprocessing (data cleansing) and other tasks that require alarge amount of effort to be invested.
For the second element, the brain wave AI
development tool, we formally released the NAIS Entrybrain wave AI analysis service in May 2021. Although
the EEG is easy to use, the brain wave data is difficult
to handle, so this service provides AI analysis ofmeasurements that our customers have taken themselves,in an SaaS format. We implement brain wave analysis
that is easier and costs less. Users first use a PGV device

to measure brain waves, which they then upload to PGVservers using an application on a tablet. The results of AI
analysis of the user’s brain wave data are then sent backto the user. We plan to continue improving NAIS Entry
functionality, to more precisely meet the needs of various
types of customers.
For the third element, the brain wave AI model, we
have consolidated various algorithms from both medical
and non-medical fields in to our NAIS Library, and areworking on visualizations of brain activity as a measure(an objective index) of brain waves. At PGV, we havealready completed development of a brain wave AI model
for sleep analysis (automatically determining stages ofsleep and generating sleep indices). In the future, beyond
development within PGV, we will strengthen our support

New Breeze Spring 202211

for external AI model development, providing our patchtype EEG and NAIS Entry service to other enterprises
and research facilities. Our brain wave AI platform is
positioned as a service platform promoting Open
Innovation in the brain-tech field.
In medical fields, we hope to use brain waves as abiomarker for mental and nervous-system disorders. For
example, brain waves could be used to diagnose dementia.
We have already prototyped a program to diagnosedementia, but we are still collaborating with the Facultyof Medicine at Osaka University to improve its reliability.
In the future, we will conduct clinical trials to verify thetechnology and hope to commercialize a brain wave AI
program for diagnosing dementia (obtaining certificationor approval as a Software as a Medical Device). We plan
to implement a system that can be used for a preliminary
diagnosis of dementia using brain waves, which canbe done by a primary-care physician, even if they are
not a specialist in dementia. This will contribute to the■ Figure 3: NAIS Entry service flow

12New Breeze Spring 2022

treatment of dementia as is needed in our hyper-aging
society.
As of FY2021, we have also begun developing a NAIS
Library for a non-medical field, visualizing people’s stateof fatigue and mindfulness, and using brain waves as an
objective index of fatigue.
Finally, I would like to introduce PGV’s long-term
vision of “Brain health management with an EEG in everyhousehold.” We hope in the future, to implement an EEG
and brain-health management program for home use, byfurther improving our EEG so that it can be used easily by
the general public, and by using evidence and brain wave
AI models obtained through initiatives in the medical andhealth-care fields. We have about 15 technical employees(data scientists, software and hardware engineers) who
are focused on implementing this long-term vision andworking daily to improve performance in the brain-techindustry.